A Novel Diagnosis Scheme against Collusive False Data Injection Attack.

Area of Science:

• Computer Science
• Cybersecurity
• Network Security

Background:

• Wireless Sensor Networks (WSNs) are vulnerable to sophisticated attacks.
• Collusive False Data Injection Attacks (CFDIAs) pose a significant threat, making defense challenging.
• Existing methods struggle to effectively identify compromised nodes during CFDIAs.

Purpose of the Study:

• To develop a robust method for identifying compromised sensors in WSNs under CFDIAs.
• To enhance the security and reliability of WSNs against coordinated false data injection.

Main Methods:

• Establishing a sensor reading prediction model.
• Employing Principal Component Analysis (PCA) to assess sensor reading consistency.
• Introducing watchdog comparators for inter-sensor validation.
• Developing a diagnostic algorithm based on consistency outcomes.

Main Results:

• The proposed method effectively identifies compromised sensors in WSNs under CFDIA.
• Simulation results demonstrate a higher probability of correct diagnosis compared to existing approaches.
• The watchdog system enhances the network's resilience against coordinated attacks.

Conclusions:

• The developed diagnostic scheme provides an effective solution for detecting CFDIAs in WSNs.
• PCA-based consistency checking is a viable strategy for identifying compromised nodes.
• The proposed approach improves overall WSN security and fault tolerance.